Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure

The size and the complexity of photovoltaic solar power plants are increasing, and it requires an advanced and robust condition monitoring systems for ensuring their reliability. This paper proposes a novel method for faults detection in photovoltaic panels employing a thermographic camera embedded in an unmanned aerial vehicle. The large amount of data generated by these systems must be processed and analyzed. This paper presents a novel approach to identify panels to detect hot spots, and to set their locations. Two novels region-based convolutional neural networks are unified to generate a robust detection structure. The main contribution is the combination of thermography and telemetry data to provide a response of the panel condition monitoring. The data are acquired and then automatically processed, allowing fault detection during the inspection. A detailed description of the methodology is presented, including the different stages to build the neural networks, i.e. the training process, the acquisition and processing of data and the outcomes generation. A thermographic inspection of a real photovoltaic solar plant is done to validate the proposed methodology. The accuracy, the efficiency and the performance of the approach under different real scenarios are evaluated statistically obtaining satisfactory results.